COMPREHENSIVE CLASSIFICATION OF RETINAL BIPOLAR NEURONS BY SINGLE-CELL TRANSCRIPTOMICS

By Karthik Shekhar, S. Lapan, Irene E. Whitney, N. Tran, Evan Z. Macosko, Monika S. Kowalczyk, Xian Adiconis, J. Levin, J. Nemesh, Melissa Goldman and 4 more. 2016

1,065 citations recorded by the source corpus.

SUMMARY Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ~25,000 BCs we derived a molecular classification that identified 15 types including all types observed previously, and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.

Subfields: Molecular Biology, Neurology

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Citations in the corpus

openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (2019, methodology): “By embedding the samples from a similar experiment on bipolar retinal cells by Shekhar et al. (2016), we can correctly map the bipolar cell clusters onto the reference embedding.”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, methodology): “Specifically, we tested these variational influence neural networks and the model neural network layer combinations (the first number is the number of layers in the variational influence neural network, and the second number is the number of layers in the model neural network): (10, 1), (10, 3), (10”
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain (2018, methodology): “Inference of Cell Types and States Using Independent Components Analysis The recognition of subtle variation among cells of the same class presents a formidable analytical challenge for unsupervised analysis (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, methodology): “ComBat We used the R package sva on the bipolar dataset following the original preprocessing steps of the bipolar paper [35].”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “Dataset 7 is composed of mouse retina data generated using the Drop-seq technology by two unassociated laboratories [39, 40].”
Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “We next compared Seurat’s performance to widely used batch correction tools that have been applied to both bulk 47 and single-cell genomics data 5 .”
Molecular Architecture of the Mouse Nervous System (2018, methodology): “Recently, single-cell RNA sequencing (scRNAseq) has emerged as a powerful method for unbiased discovery of cell types and states (Islam et al., 2011, 2013; Jaitin et al., 2014; Macosko et al., 2015; Shekhar et al., 2016; Tang et al., 2009; Tasic et al., 2016; Usoskin et al., 2014; Zeisel et al., 201”
The Human Cell Atlas (2017, background): “…data provide an initial framework (Grun et al., 2015; Grun et al., 2016; Jaitin et al., 2014; Levine et al., 2015; Macosko et al., 2015; Shekhar et al., 2016; Vallejos et al., 2015), but substantial advances will be needed in order to select the “right” features, “right” similarity…”